Transcriptome Analysis Reveals Higher Levels of Mobile Element-Associated Abnormal Gene Transcripts in Temporal Lobe Epilepsy Patients
Bibliographic record
Abstract
Mesial temporal lobe epilepsy (MTLE) is the most common form of epilepsy, and temporal lobe epilepsy patients with hippocampal sclerosis (TLE-HS) show worse drug treatment effects and prognosis. TLE has been shown to have a genetic component, but its genetic research has been mostly limited to coding sequences of genes with known association to epilepsy. Representing a major component of the genome, mobile elements (MEs) are believed to contribute to the genetic etiology of epilepsy despite limited research. We analyzed publicly available human RNA-seq-based transcriptome data to determine the role of mobile elements in epilepsy by performing de novo transcriptome assembly, followed by identification of spliced gene transcripts containing mobile element (ME) sequences (ME-transcripts), to compare their frequency across different sample groups. Significantly higher levels of ME-transcripts in hippocampal tissues of epileptic patients, particularly in TLE-HS, were observed. Among ME classes, short interspersed nuclear elements (SINEs) were shown to be the most frequent contributor to ME-transcripts, followed by long interspersed nuclear elements (LINEs) and DNA transposons. These ME sequences almost in all cases represent older MEs normally located in the intron sequences. For protein coding genes, ME sequences were mostly found in the 3′-UTR regions, with a significant portion also in the coding sequences (CDSs), leading to reading frame disruption. Genes associated with ME-transcripts showed enrichment for the mRNA splicing process and an apparent bias in epileptic transcriptomes toward neural- and epilepsy-associated genes. The findings of this study suggest that abnormal splicing involving MEs, leading to loss of functions in critical genes, plays a role in epilepsy, particularly in TLE-HS, thus providing a novel insight into the molecular mechanisms underlying epileptogenesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".